Overview

Dataset statistics

Number of variables23
Number of observations3803
Missing cells6996
Missing cells (%)8.0%
Duplicate rows126
Duplicate rows (%)3.3%
Total size in memory2.1 MiB
Average record size in memory569.9 B

Variable types

Categorical10
Text3
Numeric10

Alerts

Dataset has 126 (3.3%) duplicate rowsDuplicates
area is highly overall correlated with bathroom and 5 other fieldsHigh correlation
bathroom is highly overall correlated with area and 5 other fieldsHigh correlation
bedRoom is highly overall correlated with area and 5 other fieldsHigh correlation
built_up_area is highly overall correlated with area and 4 other fieldsHigh correlation
carpet_area is highly overall correlated with area and 5 other fieldsHigh correlation
facing is highly overall correlated with built_up_areaHigh correlation
price is highly overall correlated with area and 7 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
property_type is highly overall correlated with bedRoom and 2 other fieldsHigh correlation
servant room is highly overall correlated with bathroom and 1 other fieldsHigh correlation
super_built_up_area is highly overall correlated with area and 7 other fieldsHigh correlation
store room is highly imbalanced (56.2%)Imbalance
facing has 1105 (29.1%) missing valuesMissing
super_built_up_area has 1888 (49.6%) missing valuesMissing
built_up_area has 2070 (54.4%) missing valuesMissing
carpet_area has 1859 (48.9%) missing valuesMissing
area is highly skewed (γ1 = 30.23273447)Skewed
built_up_area is highly skewed (γ1 = 41.21758008)Skewed
carpet_area is highly skewed (γ1 = 24.7960836)Skewed
floorNum has 134 (3.5%) zerosZeros
luxury_score has 486 (12.8%) zerosZeros

Reproduction

Analysis started2025-11-26 10:53:37.094322
Analysis finished2025-11-26 10:53:52.638252
Duration15.54 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

property_type
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size197.8 KiB
flat
2943 
house
860 

Length

Max length5
Median length4
Mean length4.2261373
Min length4

Characters and Unicode

Total characters16072
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowhouse

Common Values

ValueCountFrequency (%)
flat2943
77.4%
house860
 
22.6%

Length

2025-11-26T10:53:52.738539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T10:53:52.815248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
flat2943
77.4%
house860
 
22.6%

Most occurring characters

ValueCountFrequency (%)
f2943
18.3%
l2943
18.3%
a2943
18.3%
t2943
18.3%
h860
 
5.4%
o860
 
5.4%
u860
 
5.4%
s860
 
5.4%
e860
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)16072
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f2943
18.3%
l2943
18.3%
a2943
18.3%
t2943
18.3%
h860
 
5.4%
o860
 
5.4%
u860
 
5.4%
s860
 
5.4%
e860
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16072
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f2943
18.3%
l2943
18.3%
a2943
18.3%
t2943
18.3%
h860
 
5.4%
o860
 
5.4%
u860
 
5.4%
s860
 
5.4%
e860
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16072
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f2943
18.3%
l2943
18.3%
a2943
18.3%
t2943
18.3%
h860
 
5.4%
o860
 
5.4%
u860
 
5.4%
s860
 
5.4%
e860
 
5.4%

society
Text

Distinct676
Distinct (%)17.8%
Missing1
Missing (%)< 0.1%
Memory size244.9 KiB
2025-11-26T10:53:53.149649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.922672
Min length1

Characters and Unicode

Total characters64340
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique291 ?
Unique (%)7.7%

Sample

1st rowsignature andour heights
2nd rowprime habitat
3rd rowm3m heights
4th rowsignature global city 37d ph 2
5th rowinternational city by sobha phase 2
ValueCountFrequency (%)
independent491
 
4.9%
the362
 
3.6%
dlf225
 
2.2%
park219
 
2.2%
city172
 
1.7%
global165
 
1.6%
signature161
 
1.6%
emaar159
 
1.6%
m3m156
 
1.6%
heights139
 
1.4%
Other values (783)7779
77.6%
2025-11-26T10:53:53.654004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e6935
 
10.8%
6228
 
9.7%
a6090
 
9.5%
r4355
 
6.8%
n4270
 
6.6%
i3970
 
6.2%
t3851
 
6.0%
s3627
 
5.6%
l3074
 
4.8%
o2867
 
4.5%
Other values (31)19073
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)64340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e6935
 
10.8%
6228
 
9.7%
a6090
 
9.5%
r4355
 
6.8%
n4270
 
6.6%
i3970
 
6.2%
t3851
 
6.0%
s3627
 
5.6%
l3074
 
4.8%
o2867
 
4.5%
Other values (31)19073
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)64340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e6935
 
10.8%
6228
 
9.7%
a6090
 
9.5%
r4355
 
6.8%
n4270
 
6.6%
i3970
 
6.2%
t3851
 
6.0%
s3627
 
5.6%
l3074
 
4.8%
o2867
 
4.5%
Other values (31)19073
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)64340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e6935
 
10.8%
6228
 
9.7%
a6090
 
9.5%
r4355
 
6.8%
n4270
 
6.6%
i3970
 
6.2%
t3851
 
6.0%
s3627
 
5.6%
l3074
 
4.8%
o2867
 
4.5%
Other values (31)19073
29.6%

sector
Text

Distinct132
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size217.9 KiB
2025-11-26T10:53:54.025415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length9
Mean length9.640021
Min length3

Characters and Unicode

Total characters36661
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 71
2nd rowsector 99a
3rd rowsector 65
4th rowsector 37d
5th rowsector 109
ValueCountFrequency (%)
sector3532
45.1%
road245
 
3.1%
sohna233
 
3.0%
102112
 
1.4%
85110
 
1.4%
92104
 
1.3%
6994
 
1.2%
9091
 
1.2%
8190
 
1.2%
290
 
1.2%
Other values (121)3122
39.9%
2025-11-26T10:53:54.786357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o4094
11.2%
4020
11.0%
s3917
10.7%
r3901
10.6%
e3715
10.1%
c3658
10.0%
t3608
9.8%
11121
 
3.1%
a917
 
2.5%
0824
 
2.2%
Other values (24)6886
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)36661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o4094
11.2%
4020
11.0%
s3917
10.7%
r3901
10.6%
e3715
10.1%
c3658
10.0%
t3608
9.8%
11121
 
3.1%
a917
 
2.5%
0824
 
2.2%
Other values (24)6886
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)36661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o4094
11.2%
4020
11.0%
s3917
10.7%
r3901
10.6%
e3715
10.1%
c3658
10.0%
t3608
9.8%
11121
 
3.1%
a917
 
2.5%
0824
 
2.2%
Other values (24)6886
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)36661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o4094
11.2%
4020
11.0%
s3917
10.7%
r3901
10.6%
e3715
10.1%
c3658
10.0%
t3608
9.8%
11121
 
3.1%
a917
 
2.5%
0824
 
2.2%
Other values (24)6886
18.8%

price
Real number (ℝ)

High correlation 

Distinct473
Distinct (%)12.5%
Missing18
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.5058045
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2025-11-26T10:55:40.590317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.94
median1.5
Q32.7
95-th percentile8.49
Maximum31.5
Range31.43
Interquartile range (IQR)1.76

Descriptive statistics

Standard deviation2.9501212
Coefficient of variation (CV)1.177315
Kurtosis15.257819
Mean2.5058045
Median Absolute Deviation (MAD)0.71
Skewness3.3113347
Sum9484.47
Variance8.703215
MonotonicityNot monotonic
2025-11-26T10:55:40.733029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2583
 
2.2%
0.968
 
1.8%
1.266
 
1.7%
1.166
 
1.7%
1.566
 
1.7%
1.463
 
1.7%
1.360
 
1.6%
0.9558
 
1.5%
256
 
1.5%
151
 
1.3%
Other values (463)3148
82.8%
ValueCountFrequency (%)
0.071
 
< 0.1%
0.161
 
< 0.1%
0.171
 
< 0.1%
0.191
 
< 0.1%
0.29
0.2%
0.216
0.2%
0.229
0.2%
0.231
 
< 0.1%
0.247
0.2%
0.2511
0.3%
ValueCountFrequency (%)
31.51
 
< 0.1%
27.51
 
< 0.1%
262
0.1%
251
 
< 0.1%
241
 
< 0.1%
231
 
< 0.1%
221
 
< 0.1%
203
0.1%
19.52
0.1%
193
0.1%

price_per_sqft
Real number (ℝ)

High correlation 

Distinct2651
Distinct (%)70.0%
Missing18
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13800.168
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2025-11-26T10:55:40.948786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4718.2
Q16808
median9000
Q313765
95-th percentile33308.2
Maximum600000
Range599996
Interquartile range (IQR)6957

Descriptive statistics

Standard deviation23052.006
Coefficient of variation (CV)1.6704149
Kurtosis187.04187
Mean13800.168
Median Absolute Deviation (MAD)2758
Skewness11.43922
Sum52233635
Variance5.3139496 × 108
MonotonicityNot monotonic
2025-11-26T10:55:41.161544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000028
 
0.7%
800019
 
0.5%
1250017
 
0.4%
500017
 
0.4%
750014
 
0.4%
1111114
 
0.4%
666614
 
0.4%
833313
 
0.3%
2222213
 
0.3%
600011
 
0.3%
Other values (2641)3625
95.3%
(Missing)18
 
0.5%
ValueCountFrequency (%)
41
< 0.1%
51
< 0.1%
71
< 0.1%
91
< 0.1%
531
< 0.1%
571
< 0.1%
582
0.1%
601
< 0.1%
611
< 0.1%
791
< 0.1%
ValueCountFrequency (%)
6000001
< 0.1%
4000001
< 0.1%
3157891
< 0.1%
3083331
< 0.1%
2909481
< 0.1%
2833331
< 0.1%
2666661
< 0.1%
2611941
< 0.1%
2453981
< 0.1%
2416661
< 0.1%

area
Real number (ℝ)

High correlation  Skewed 

Distinct1312
Distinct (%)34.7%
Missing18
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2845.9995
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2025-11-26T10:55:41.383123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile519
Q11220
median1725
Q32295
95-th percentile4200
Maximum875000
Range874950
Interquartile range (IQR)1075

Descriptive statistics

Standard deviation22783.349
Coefficient of variation (CV)8.0053947
Kurtosis974.19183
Mean2845.9995
Median Absolute Deviation (MAD)525
Skewness30.232734
Sum10772108
Variance5.1908099 × 108
MonotonicityNot monotonic
2025-11-26T10:55:41.589260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165055
 
1.4%
135051
 
1.3%
180048
 
1.3%
195044
 
1.2%
324043
 
1.1%
270039
 
1.0%
90039
 
1.0%
200035
 
0.9%
225025
 
0.7%
240025
 
0.7%
Other values (1302)3381
88.9%
ValueCountFrequency (%)
504
0.1%
551
 
< 0.1%
561
 
< 0.1%
571
 
< 0.1%
602
0.1%
611
 
< 0.1%
672
0.1%
701
 
< 0.1%
721
 
< 0.1%
761
 
< 0.1%
ValueCountFrequency (%)
8750001
< 0.1%
6428571
< 0.1%
6200001
< 0.1%
5666671
< 0.1%
2155171
< 0.1%
989781
< 0.1%
827811
< 0.1%
655172
0.1%
652611
< 0.1%
582281
< 0.1%
Distinct2355
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Memory size382.1 KiB
2025-11-26T10:55:42.117701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length124
Median length119
Mean length53.841967
Min length12

Characters and Unicode

Total characters204761
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1780 ?
Unique (%)46.8%

Sample

1st rowSuper Built up area 600(55.74 sq.m.)Carpet area: 514 sq.ft. (47.75 sq.m.)
2nd rowCarpet area: 550 (51.1 sq.m.)
3rd rowSuper Built up area 1260(117.06 sq.m.)
4th rowBuilt Up area: 1535 (142.61 sq.m.)
5th rowPlot area 500(418.06 sq.m.)
ValueCountFrequency (%)
area5728
18.5%
sq.m3779
12.2%
up3102
 
10.0%
built2393
 
7.7%
super1915
 
6.2%
sq.ft1779
 
5.7%
sq.m.)carpet1208
 
3.9%
carpet732
 
2.4%
sq.m.)built707
 
2.3%
plot682
 
2.2%
Other values (2846)8965
28.9%
2025-11-26T10:55:42.883068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27187
 
13.3%
.20907
 
10.2%
a13536
 
6.6%
r9723
 
4.7%
e9587
 
4.7%
19460
 
4.6%
s7747
 
3.8%
q7611
 
3.7%
t7507
 
3.7%
p6961
 
3.4%
Other values (25)84535
41.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)204761
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
27187
 
13.3%
.20907
 
10.2%
a13536
 
6.6%
r9723
 
4.7%
e9587
 
4.7%
19460
 
4.6%
s7747
 
3.8%
q7611
 
3.7%
t7507
 
3.7%
p6961
 
3.4%
Other values (25)84535
41.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)204761
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
27187
 
13.3%
.20907
 
10.2%
a13536
 
6.6%
r9723
 
4.7%
e9587
 
4.7%
19460
 
4.6%
s7747
 
3.8%
q7611
 
3.7%
t7507
 
3.7%
p6961
 
3.4%
Other values (25)84535
41.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)204761
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
27187
 
13.3%
.20907
 
10.2%
a13536
 
6.6%
r9723
 
4.7%
e9587
 
4.7%
19460
 
4.6%
s7747
 
3.8%
q7611
 
3.7%
t7507
 
3.7%
p6961
 
3.4%
Other values (25)84535
41.3%

bedRoom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3381541
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2025-11-26T10:55:43.046844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8767336
Coefficient of variation (CV)0.56220699
Kurtosis18.610254
Mean3.3381541
Median Absolute Deviation (MAD)1
Skewness3.511539
Sum12695
Variance3.5221288
MonotonicityNot monotonic
2025-11-26T10:55:43.570099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
31545
40.6%
2993
26.1%
4676
17.8%
5213
 
5.6%
1130
 
3.4%
675
 
2.0%
941
 
1.1%
830
 
0.8%
1228
 
0.7%
728
 
0.7%
Other values (9)44
 
1.2%
ValueCountFrequency (%)
1130
 
3.4%
2993
26.1%
31545
40.6%
4676
17.8%
5213
 
5.6%
675
 
2.0%
728
 
0.7%
830
 
0.8%
941
 
1.1%
1020
 
0.5%
ValueCountFrequency (%)
211
 
< 0.1%
201
 
< 0.1%
192
 
0.1%
182
 
0.1%
1612
0.3%
141
 
< 0.1%
134
 
0.1%
1228
0.7%
111
 
< 0.1%
1020
0.5%

bathroom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4054694
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2025-11-26T10:55:43.673054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9304562
Coefficient of variation (CV)0.56686936
Kurtosis17.745175
Mean3.4054694
Median Absolute Deviation (MAD)1
Skewness3.2570832
Sum12951
Variance3.7266613
MonotonicityNot monotonic
2025-11-26T10:55:43.779980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
31112
29.2%
21105
29.1%
4839
22.1%
5299
 
7.9%
1160
 
4.2%
6120
 
3.2%
941
 
1.1%
741
 
1.1%
826
 
0.7%
1222
 
0.6%
Other values (9)38
 
1.0%
ValueCountFrequency (%)
1160
 
4.2%
21105
29.1%
31112
29.2%
4839
22.1%
5299
 
7.9%
6120
 
3.2%
741
 
1.1%
826
 
0.7%
941
 
1.1%
109
 
0.2%
ValueCountFrequency (%)
211
 
< 0.1%
203
 
0.1%
184
 
0.1%
173
 
0.1%
168
 
0.2%
142
 
0.1%
134
 
0.1%
1222
0.6%
114
 
0.1%
109
0.2%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size187.0 KiB
3+
1202 
3
1110 
2
925 
1
376 
0
190 

Length

Max length2
Median length1
Mean length1.3160663
Min length1

Characters and Unicode

Total characters5005
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3+1202
31.6%
31110
29.2%
2925
24.3%
1376
 
9.9%
0190
 
5.0%

Length

2025-11-26T10:55:43.895254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T10:55:43.978395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
32312
60.8%
2925
24.3%
1376
 
9.9%
0190
 
5.0%

Most occurring characters

ValueCountFrequency (%)
32312
46.2%
+1202
24.0%
2925
18.5%
1376
 
7.5%
0190
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)5005
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
32312
46.2%
+1202
24.0%
2925
18.5%
1376
 
7.5%
0190
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5005
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
32312
46.2%
+1202
24.0%
2925
18.5%
1376
 
7.5%
0190
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5005
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
32312
46.2%
+1202
24.0%
2925
18.5%
1376
 
7.5%
0190
 
3.8%

floorNum
Real number (ℝ)

Zeros 

Distinct43
Distinct (%)1.1%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.8102537
Minimum0
Maximum51
Zeros134
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2025-11-26T10:55:44.101261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0275551
Coefficient of variation (CV)0.88507056
Kurtosis4.5493229
Mean6.8102537
Median Absolute Deviation (MAD)3
Skewness1.6987333
Sum25770
Variance36.33142
MonotonicityNot monotonic
2025-11-26T10:55:44.229161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3513
13.5%
2506
13.3%
1365
 
9.6%
4328
 
8.6%
8197
 
5.2%
6187
 
4.9%
10186
 
4.9%
7183
 
4.8%
5177
 
4.7%
9170
 
4.5%
Other values (33)972
25.6%
ValueCountFrequency (%)
0134
 
3.5%
1365
9.6%
2506
13.3%
3513
13.5%
4328
8.6%
5177
 
4.7%
6187
 
4.9%
7183
 
4.8%
8197
 
5.2%
9170
 
4.5%
ValueCountFrequency (%)
511
 
< 0.1%
451
 
< 0.1%
441
 
< 0.1%
432
0.1%
402
0.1%
392
0.1%
381
 
< 0.1%
352
0.1%
342
0.1%
334
0.1%

facing
Categorical

High correlation  Missing 

Distinct8
Distinct (%)0.3%
Missing1105
Missing (%)29.1%
Memory size207.7 KiB
East
642 
North-East
639 
North
398 
West
255 
South
233 
Other values (3)
531 

Length

Max length10
Median length5
Mean length6.8358043
Min length4

Characters and Unicode

Total characters18443
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth-West
2nd rowNorth-East
3rd rowNorth-East
4th rowEast
5th rowEast

Common Values

ValueCountFrequency (%)
East642
16.9%
North-East639
16.8%
North398
 
10.5%
West255
 
6.7%
South233
 
6.1%
North-West200
 
5.3%
South-East174
 
4.6%
South-West157
 
4.1%
(Missing)1105
29.1%

Length

2025-11-26T10:55:44.352349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T10:55:44.450521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
east642
23.8%
north-east639
23.7%
north398
14.8%
west255
 
9.5%
south233
 
8.6%
north-west200
 
7.4%
south-east174
 
6.4%
south-west157
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t3868
21.0%
s2067
11.2%
o1801
9.8%
h1801
9.8%
E1455
 
7.9%
a1455
 
7.9%
N1237
 
6.7%
r1237
 
6.7%
-1170
 
6.3%
W612
 
3.3%
Other values (3)1740
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)18443
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t3868
21.0%
s2067
11.2%
o1801
9.8%
h1801
9.8%
E1455
 
7.9%
a1455
 
7.9%
N1237
 
6.7%
r1237
 
6.7%
-1170
 
6.3%
W612
 
3.3%
Other values (3)1740
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18443
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t3868
21.0%
s2067
11.2%
o1801
9.8%
h1801
9.8%
E1455
 
7.9%
a1455
 
7.9%
N1237
 
6.7%
r1237
 
6.7%
-1170
 
6.3%
W612
 
3.3%
Other values (3)1740
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18443
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t3868
21.0%
s2067
11.2%
o1801
9.8%
h1801
9.8%
E1455
 
7.9%
a1455
 
7.9%
N1237
 
6.7%
r1237
 
6.7%
-1170
 
6.3%
W612
 
3.3%
Other values (3)1740
9.4%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size230.4 KiB
Relatively New
1676 
New Property
626 
Moderately Old
575 
Undefined
484 
Old Property
310 

Length

Max length18
Median length14
Mean length13.010255
Min length9

Characters and Unicode

Total characters49478
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRelatively New
2nd rowRelatively New
3rd rowUndefined
4th rowUndefined
5th rowRelatively New

Common Values

ValueCountFrequency (%)
Relatively New1676
44.1%
New Property626
 
16.5%
Moderately Old575
 
15.1%
Undefined484
 
12.7%
Old Property310
 
8.2%
Under Construction132
 
3.5%

Length

2025-11-26T10:55:44.595379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T10:55:44.677472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
new2302
32.3%
relatively1676
23.5%
property936
13.1%
old885
 
12.4%
moderately575
 
8.1%
undefined484
 
6.8%
under132
 
1.9%
construction132
 
1.9%

Most occurring characters

ValueCountFrequency (%)
e8840
17.9%
l4812
 
9.7%
t3451
 
7.0%
3319
 
6.7%
y3187
 
6.4%
r2711
 
5.5%
d2560
 
5.2%
N2302
 
4.7%
w2302
 
4.7%
i2292
 
4.6%
Other values (15)13702
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)49478
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e8840
17.9%
l4812
 
9.7%
t3451
 
7.0%
3319
 
6.7%
y3187
 
6.4%
r2711
 
5.5%
d2560
 
5.2%
N2302
 
4.7%
w2302
 
4.7%
i2292
 
4.6%
Other values (15)13702
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)49478
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e8840
17.9%
l4812
 
9.7%
t3451
 
7.0%
3319
 
6.7%
y3187
 
6.4%
r2711
 
5.5%
d2560
 
5.2%
N2302
 
4.7%
w2302
 
4.7%
i2292
 
4.6%
Other values (15)13702
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)49478
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e8840
17.9%
l4812
 
9.7%
t3451
 
7.0%
3319
 
6.7%
y3187
 
6.4%
r2711
 
5.5%
d2560
 
5.2%
N2302
 
4.7%
w2302
 
4.7%
i2292
 
4.6%
Other values (15)13702
27.7%

super_built_up_area
Real number (ℝ)

High correlation  Missing 

Distinct593
Distinct (%)31.0%
Missing1888
Missing (%)49.6%
Infinite0
Infinite (%)0.0%
Mean1921.6583
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2025-11-26T10:55:44.813428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile761.9
Q11457
median1828
Q32215
95-th percentile3187.1
Maximum10000
Range9911
Interquartile range (IQR)758

Descriptive statistics

Standard deviation767.16017
Coefficient of variation (CV)0.39921779
Kurtosis10.083066
Mean1921.6583
Median Absolute Deviation (MAD)372
Skewness1.8232285
Sum3679975.5
Variance588534.73
MonotonicityNot monotonic
2025-11-26T10:55:44.953975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
195038
 
1.0%
165038
 
1.0%
200026
 
0.7%
157825
 
0.7%
215023
 
0.6%
164022
 
0.6%
240820
 
0.5%
135019
 
0.5%
190019
 
0.5%
193018
 
0.5%
Other values (583)1667
43.8%
(Missing)1888
49.6%
ValueCountFrequency (%)
891
< 0.1%
1451
< 0.1%
1611
< 0.1%
2151
< 0.1%
2161
< 0.1%
3251
< 0.1%
3401
< 0.1%
3521
< 0.1%
3801
< 0.1%
4061
< 0.1%
ValueCountFrequency (%)
100001
< 0.1%
69261
< 0.1%
60001
< 0.1%
58002
0.1%
55141
< 0.1%
53502
0.1%
52002
0.1%
48901
< 0.1%
48572
0.1%
48482
0.1%

built_up_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct644
Distinct (%)37.2%
Missing2070
Missing (%)54.4%
Infinite0
Infinite (%)0.0%
Mean2360.2414
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2025-11-26T10:55:45.091296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile246.4
Q11100
median1650
Q32399
95-th percentile4662
Maximum737147
Range737145
Interquartile range (IQR)1299

Descriptive statistics

Standard deviation17719.603
Coefficient of variation (CV)7.5075385
Kurtosis1710.1077
Mean2360.2414
Median Absolute Deviation (MAD)642
Skewness41.21758
Sum4090298.4
Variance3.1398434 × 108
MonotonicityNot monotonic
2025-11-26T10:55:45.227059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180041
 
1.1%
324037
 
1.0%
135034
 
0.9%
190034
 
0.9%
270033
 
0.9%
90028
 
0.7%
160026
 
0.7%
200025
 
0.7%
130025
 
0.7%
170023
 
0.6%
Other values (634)1427
37.5%
(Missing)2070
54.4%
ValueCountFrequency (%)
21
 
< 0.1%
141
 
< 0.1%
301
 
< 0.1%
331
 
< 0.1%
503
0.1%
531
 
< 0.1%
551
 
< 0.1%
561
 
< 0.1%
571
 
< 0.1%
605
0.1%
ValueCountFrequency (%)
7371471
 
< 0.1%
135001
 
< 0.1%
112861
 
< 0.1%
95001
 
< 0.1%
90007
0.2%
87751
 
< 0.1%
82861
 
< 0.1%
8067.81
 
< 0.1%
80001
 
< 0.1%
75002
 
0.1%

carpet_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct733
Distinct (%)37.7%
Missing1859
Missing (%)48.9%
Infinite0
Infinite (%)0.0%
Mean2483.4669
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2025-11-26T10:55:45.362387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile348.3
Q1824
median1294
Q31786.25
95-th percentile2945.8
Maximum607936
Range607921
Interquartile range (IQR)962.25

Descriptive statistics

Standard deviation22375.239
Coefficient of variation (CV)9.0096787
Kurtosis627.83936
Mean2483.4669
Median Absolute Deviation (MAD)472
Skewness24.796084
Sum4827859.7
Variance5.0065133 × 108
MonotonicityNot monotonic
2025-11-26T10:55:45.495843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140042
 
1.1%
160036
 
0.9%
180036
 
0.9%
120032
 
0.8%
150030
 
0.8%
165028
 
0.7%
135028
 
0.7%
130023
 
0.6%
145023
 
0.6%
100022
 
0.6%
Other values (723)1644
43.2%
(Missing)1859
48.9%
ValueCountFrequency (%)
151
 
< 0.1%
331
 
< 0.1%
481
 
< 0.1%
501
 
< 0.1%
591
 
< 0.1%
601
 
< 0.1%
661
 
< 0.1%
721
 
< 0.1%
76.443
0.1%
77.312
0.1%
ValueCountFrequency (%)
6079361
< 0.1%
5692431
< 0.1%
5143961
< 0.1%
645291
< 0.1%
644121
< 0.1%
581411
< 0.1%
549171
< 0.1%
488111
< 0.1%
459661
< 0.1%
344011
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size185.8 KiB
0
3082 
1
721 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
03082
81.0%
1721
 
19.0%

Length

2025-11-26T10:55:45.638901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T10:55:45.708954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
03082
81.0%
1721
 
19.0%

Most occurring characters

ValueCountFrequency (%)
03082
81.0%
1721
 
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03082
81.0%
1721
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03082
81.0%
1721
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03082
81.0%
1721
 
19.0%

servant room
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size185.8 KiB
0
2446 
1
1357 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
02446
64.3%
11357
35.7%

Length

2025-11-26T10:55:45.800076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T10:55:45.870756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
02446
64.3%
11357
35.7%

Most occurring characters

ValueCountFrequency (%)
02446
64.3%
11357
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02446
64.3%
11357
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02446
64.3%
11357
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02446
64.3%
11357
35.7%

store room
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size185.8 KiB
0
3459 
1
 
344

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
03459
91.0%
1344
 
9.0%

Length

2025-11-26T10:55:45.962747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T10:55:46.038017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
03459
91.0%
1344
 
9.0%

Most occurring characters

ValueCountFrequency (%)
03459
91.0%
1344
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03459
91.0%
1344
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03459
91.0%
1344
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03459
91.0%
1344
 
9.0%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size185.8 KiB
0
3140 
1
663 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
03140
82.6%
1663
 
17.4%

Length

2025-11-26T10:55:46.127830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T10:55:46.195213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
03140
82.6%
1663
 
17.4%

Most occurring characters

ValueCountFrequency (%)
03140
82.6%
1663
 
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03140
82.6%
1663
 
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03140
82.6%
1663
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03140
82.6%
1663
 
17.4%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size185.8 KiB
0
3382 
1
421 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
03382
88.9%
1421
 
11.1%

Length

2025-11-26T10:55:46.284329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T10:55:46.357670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
03382
88.9%
1421
 
11.1%

Most occurring characters

ValueCountFrequency (%)
03382
88.9%
1421
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03382
88.9%
1421
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03382
88.9%
1421
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03382
88.9%
1421
 
11.1%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size185.8 KiB
1
2534 
0
1057 
2
 
212

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
12534
66.6%
01057
27.8%
2212
 
5.6%

Length

2025-11-26T10:55:46.446281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T10:55:46.519882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
12534
66.6%
01057
27.8%
2212
 
5.6%

Most occurring characters

ValueCountFrequency (%)
12534
66.6%
01057
27.8%
2212
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12534
66.6%
01057
27.8%
2212
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12534
66.6%
01057
27.8%
2212
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12534
66.6%
01057
27.8%
2212
 
5.6%

luxury_score
Real number (ℝ)

Zeros 

Distinct161
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.947936
Minimum0
Maximum174
Zeros486
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2025-11-26T10:55:46.630379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median58
Q3109
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)78

Descriptive statistics

Standard deviation52.821789
Coefficient of variation (CV)0.74451481
Kurtosis-0.85533655
Mean70.947936
Median Absolute Deviation (MAD)37
Skewness0.47028839
Sum269815
Variance2790.1414
MonotonicityNot monotonic
2025-11-26T10:55:46.764847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0486
 
12.8%
49353
 
9.3%
174196
 
5.2%
4462
 
1.6%
3858
 
1.5%
7256
 
1.5%
16555
 
1.4%
6050
 
1.3%
3749
 
1.3%
4246
 
1.2%
Other values (151)2392
62.9%
ValueCountFrequency (%)
0486
12.8%
56
 
0.2%
66
 
0.2%
743
 
1.1%
830
 
0.8%
99
 
0.2%
127
 
0.2%
1310
 
0.3%
1412
 
0.3%
1543
 
1.1%
ValueCountFrequency (%)
174196
5.2%
1691
 
< 0.1%
1689
 
0.2%
16721
 
0.6%
16611
 
0.3%
16555
 
1.4%
1613
 
0.1%
16028
 
0.7%
15923
 
0.6%
15834
 
0.9%

Interactions

2025-11-26T10:55:36.871591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:24.692924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:25.799180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:27.616453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:28.991009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:30.717603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:32.167383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:33.275112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:34.355183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:35.486262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:36.982139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:24.806199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:25.902249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:27.734062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:29.161711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:30.852706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:32.276025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:33.385960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:34.471749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:35.594071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:37.102409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:24.916344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:26.014678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:27.840670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:29.333600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:30.987732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:32.384016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:33.500516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:34.586272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:35.715499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:37.221687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:25.023298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:26.121713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:27.939475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:29.483866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:31.106480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:32.487737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:33.601934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:34.693642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:36.101053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:37.345851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:25.138377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:26.242165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:28.051824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:29.643115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:31.225325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:32.600073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:33.715904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:34.815956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:36.221423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:37.468882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:25.251316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:26.361393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:28.173327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:29.816607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:31.346840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:32.715107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:33.825296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:34.936319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:36.336021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:37.579037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:25.354313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:26.467589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:28.340696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:29.985812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:31.680487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:32.812122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:33.929647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:35.055345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:36.440822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:37.688355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:25.455643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:27.264278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:28.499782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:30.157681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:31.787525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:32.929381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:34.048975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:35.152097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:36.546576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:37.807452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:25.569014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:27.383112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:28.654430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:30.341996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:31.917217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:33.059406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:34.142741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:35.272447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:36.643562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:37.916843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:25.687793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:27.498382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:28.831125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:30.523192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:32.051297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:33.159534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:34.245278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:35.370611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T10:55:36.751255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-26T10:55:46.891785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
agePossessionareabalconybathroombedRoombuilt_up_areacarpet_areafacingfloorNumfurnishing_typeluxury_scoreotherspooja roompriceprice_per_sqftproperty_typeservant roomstore roomstudy roomsuper_built_up_area
agePossession1.0000.0000.2280.1080.1250.0000.0000.0920.1220.2190.2250.1030.1870.1000.0470.3460.2880.1440.1140.081
area0.0001.0000.0100.6920.6310.8370.8060.0220.1150.0430.2590.0420.0380.7450.2060.0290.0150.0390.0180.949
balcony0.2280.0101.0000.2250.1750.0000.0250.0140.0790.1800.2230.0810.1950.1360.0330.2100.4390.1430.1820.304
bathroom0.1080.6920.2251.0000.8630.4680.6070.044-0.0060.1940.1810.0690.2830.7210.4060.4720.5180.2440.1750.822
bedRoom0.1250.6310.1750.8631.0000.3850.5770.031-0.1000.1660.0620.0770.2920.6830.4110.5940.3170.2220.1560.802
built_up_area0.0000.8370.0000.4680.3851.0000.9681.0000.0880.0890.2900.0000.0000.6040.1290.0000.0000.0000.0000.927
carpet_area0.0000.8060.0250.6070.5770.9681.0000.0000.1510.0000.2360.0170.0000.6220.1430.0000.0000.0000.0040.895
facing0.0920.0220.0140.0440.0311.0000.0001.0000.0000.0570.0640.0000.0280.0220.0000.0910.0420.0330.0000.000
floorNum0.1220.1150.079-0.006-0.1000.0880.1510.0001.0000.0280.2230.0280.1000.003-0.1200.4750.0800.1090.0780.155
furnishing_type0.2190.0430.1800.1940.1660.0890.0000.0570.0281.0000.2390.0570.2150.1730.0200.0890.2680.1610.1370.133
luxury_score0.2250.2590.2230.1810.0620.2900.2360.0640.2230.2391.0000.1730.1910.2170.0570.3180.3470.2270.1850.227
others0.1030.0420.0810.0690.0770.0000.0170.0000.0280.0570.1731.0000.0340.0330.0350.0240.0000.1030.0320.082
pooja room0.1870.0380.1950.2830.2920.0000.0000.0280.1000.2150.1910.0341.0000.3340.0440.2540.2510.3050.3140.154
price0.1000.7450.1360.7210.6830.6040.6220.0220.0030.1730.2170.0330.3341.0000.7430.5410.3680.3000.2420.774
price_per_sqft0.0470.2060.0330.4060.4110.1290.1430.000-0.1200.0200.0570.0350.0440.7431.0000.1990.0410.0000.0290.286
property_type0.3460.0290.2100.4720.5940.0000.0000.0910.4750.0890.3180.0240.2540.5410.1991.0000.0700.2420.1281.000
servant room0.2880.0150.4390.5180.3170.0000.0000.0420.0800.2680.3470.0000.2510.3680.0410.0701.0000.1610.1820.587
store room0.1440.0390.1430.2440.2220.0000.0000.0330.1090.1610.2270.1030.3050.3000.0000.2420.1611.0000.2220.043
study room0.1140.0180.1820.1750.1560.0000.0040.0000.0780.1370.1850.0320.3140.2420.0290.1280.1820.2221.0000.116
super_built_up_area0.0810.9490.3040.8220.8020.9270.8950.0000.1550.1330.2270.0820.1540.7740.2861.0000.5870.0430.1161.000

Missing values

2025-11-26T10:55:38.124948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-26T10:55:38.346190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-26T10:55:38.562907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0flatsignature andour heightssector 710.6010000.0600.0Super Built up area 600(55.74 sq.m.)Carpet area: 514 sq.ft. (47.75 sq.m.)2215.0South-WestRelatively New600.0NaN514.000010159
1flatprime habitatsector 99a0.264745.0548.0Carpet area: 550 (51.1 sq.m.)2216.0North-EastRelatively NewNaNNaN550.000001179
2flatm3m heightssector 651.9915793.01260.0Super Built up area 1260(117.06 sq.m.)22226.0NaNUndefined1260.0NaNNaN00000148
3flatsignature global city 37d ph 2sector 37d1.368846.01537.0Built Up area: 1535 (142.61 sq.m.)3331.0North-EastUndefinedNaN1535.0NaN00011137
4houseinternational city by sobha phase 2sector 10912.5026667.04687.0Plot area 500(418.06 sq.m.)5633.0EastRelatively NewNaN4500.0NaN11110163
5flatkibithu villassector 483.4512777.02700.0Carpet area: 2700 (250.84 sq.m.)443+2.0NaNUndefinedNaNNaN2700.00000010
6flatm3m skycitysector 653.5017039.02054.0Carpet area: 2054 (190.82 sq.m.)33315.0EastNew PropertyNaNNaN2054.0110100166
7flatemaar palm gardenssector 831.3011868.01095.0Super Built up area 1720(159.79 sq.m.)Carpet area: 1095.3 sq.ft. (101.76 sq.m.)33316.0NorthRelatively New1720.0NaN1095.3010000174
8flattulip violetsector 691.368618.01578.0Super Built up area 1578(146.6 sq.m.)33212.0WestRelatively New1578.0NaNNaN00010049
9houseindependentsector 1100.346800.0500.0Built Up area: 500 (46.45 sq.m.)1101.0NaNUndefinedNaN500.0NaN0000010
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3793flatrof anandasector 950.555500.01000.0Super Built up area 1000(92.9 sq.m.)Carpet area: 745 sq.ft. (69.21 sq.m.)32214.0EastNew Property1000.0NaN745.010000230
3794flatshree vardhman florasector 900.904615.01950.0Super Built up area 1950(181.16 sq.m.)3434.0EastRelatively New1950.0NaNNaN010001165
3795flatcorona optussector 37c1.226912.01765.0Super Built up area 1765(163.97 sq.m.)Built Up area: 1665 sq.ft. (154.68 sq.m.)Carpet area: 1565 sq.ft. (145.39 sq.m.)3435.0South-EastRelatively New1765.01665.01565.0000001149
3796flatsare homessector 920.584098.01415.0Super Built up area 1415(131.46 sq.m.)3333.0North-EastNew Property1415.0NaNNaN00000154
3797flatsignature global parksohna road0.688252.0824.0Carpet area: 824 (76.55 sq.m.)2223.0NaNNew PropertyNaNNaN824.0000001113
3798flatashiana anmolsector 331.108627.01275.0Built Up area: 1275 (118.45 sq.m.)Carpet area: 795 sq.ft. (73.86 sq.m.)2224.0North-EastUndefinedNaN1275.0795.00000010
3799flatramshanti cooperative societysector 521.767242.02430.0Super Built up area 2430(225.75 sq.m.)443+12.0NaNNew Property2430.0NaNNaN0000010
3800flatsignature global parksohna road0.556690.0822.0Carpet area: 822 (76.37 sq.m.)2233.0EastUndefinedNaNNaN822.000000158
3801flatdlf regal gardenssector 901.657449.02215.0Super Built up area 2215(205.78 sq.m.)Built Up area: 2214 sq.ft. (205.69 sq.m.)Carpet area: 2213 sq.ft. (205.59 sq.m.)443+15.0EastRelatively New2215.02214.02213.0000001158
3802houseindependentsector 1051.3011555.01125.0Plot area 1125(104.52 sq.m.)3312.0NorthModerately OldNaN1125.0NaN00000116

Duplicate rows

Most frequently occurring

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score# duplicates
0flatambience caitrionasector 2414.00200000.0700.0Built Up area: 700 (65.03 sq.m.)4533.0EastUndefinedNaN700.0NaN00000102
1flatansal heights 86sector 860.905325.01690.0Built Up area: 1690 (157.01 sq.m.)33210.0NaNNew PropertyNaN1690.0NaN000001292
2flatansal heights 86sector 861.304666.02786.0Super Built up area 2786(258.83 sq.m.)46211.0EastNew Property2786.0NaNNaN010011862
3flatansal housing highland parksector 1030.886429.01369.0Super Built up area 1361(126.44 sq.m.)2233.0NaNNew Property1361.0NaNNaN000001522
4flatantriksh heightssector 840.855556.01530.0Super Built up area 1350(125.42 sq.m.)22310.0North-WestNew Property1350.0NaNNaN100011242
5flatapartmentsector 920.754687.01600.0Carpet area: 1600 (148.64 sq.m.)3432.0EastModerately OldNaNNaN1600.01000011132
6flatashiana anmolsohna road0.8811125.0791.0Super Built up area 1275(118.45 sq.m.)Carpet area: 791 sq.ft. (73.49 sq.m.)22213.0EastRelatively New1275.0NaN791.00000021272
7flatassotech blithsector 990.926739.01365.0Super Built up area 1365(126.81 sq.m.)223+22.0NaNUnder Construction1365.0NaNNaN000001562
8flatassotech blithsector 991.906702.02835.0Built Up area: 2835 (263.38 sq.m.)443+2.0North-EastUndefinedNaN2835.0NaN000001512
9flatats tourmalinesector 1092.308897.02585.0Super Built up area 2585(240.15 sq.m.)343+10.0EastNew Property2585.0NaNNaN010011742